The most dynamic electromagnetic coupling between the magnetosphere and ionosphere occurs in the polar upper atmosphere. It is critical to quantify the electromagnetic energy and momentum input associated with this coupling as its impacts on the ionosphere and thermosphere system are global and major, often leading to considerable disturbances in near‐Earth space environments. The current general circulation models of the upper atmosphere exhibit systematic biases that can be attributed to an inadequate representation of the Joule heating rate resulting from unaccounted stochastic fluctuations of electric fields associated with the magnetosphere‐ionosphere coupling. These biases exist regardless of geomagnetic activity levels. To overcome this limitation, a new multiresolution random field modeling approach is developed, and the efficacy of the approach is demonstrated using Super Dual Auroral Radar Network (SuperDARN) data carefully curated for the study during a largely quiet 4‐hour period on February 29, 2012. Regional small‐scale electrostatic fields sampled at different resolutions from a probabilistic distribution of electric field variability conditioned on actual SuperDARN LOS observations exhibit considerably more localized fine‐scale features in comparison to global large‐scale fields modeled using the SuperDARN Assimilative Mapping procedure. The overall hemispherically integrated Joule heating rate is increased by a factor of about 1.5 due to the effect of random regional small‐scale electric fields, which is close to the lower end of arbitrarily adjusted Joule heating multiplicative factor of 1.5 and 2.5 typically used in upper atmosphere general circulation models. The study represents an important step toward a data‐driven ensemble modeling of magnetosphere‐ionosphere‐atmosphere coupling processes.
Far ultraviolet (FUV) imaging of the aurora from space provides great insight into the dynamic coupling of the atmosphere, ionosphere, and magnetosphere on global scales. To gain a quantitative understanding of these coupling processes, the global distribution of auroral energy flux is required, but the inversion of FUV emission to derive precipitating auroral particles' energy flux is not straightforward. Furthermore, the spatial coverage of FUV imaging from Low Earth Orbit (LEO) altitudes is often insufficient to achieve global mapping of this important parameter. This study seeks to fill these gaps left by the current geospace observing system using a combination of data assimilation and machine learning techniques. Specifically, this paper presents a new data‐driven modeling approach to create instantaneous, global assimilative mappings of auroral electron total energy flux from Lyman‐Birge‐Hopfield (LBH) emission data from the Defense Meteorological System Program (DMSP) Special Sensor Ultraviolet Spectrographic Imager (SSUSI). We take a two‐step approach; the creation of assimilative maps of LBH emission using optimal interpolation, followed by the conversion to energy flux using a neural network model trained with conjunction observations of in‐situ auroral particles and LBH emission from the DMSP Special Sensor J and SSUSI instruments. The paper demonstrates the feasibility of this approach with a model prototype built with DMSP data from 17 February 2014 to 23 February 2014. This study serves as a blueprint for a future comprehensive data‐driven model of auroral energy flux that is complementary to traditional inversion techniques to take advantage of FUV imaging from LEO platforms for global assimilative mapping of auroral energy flux.
more » « less- NSF-PAR ID:
- 10446335
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Space Physics
- Volume:
- 127
- Issue:
- 3
- ISSN:
- 2169-9380
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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